


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase			=				logging.get_logger(__name__)

lowerCAmelCase			=				{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     Any     =					'''vit_msn'''

							def __init__(				self:      Any       ,		UpperCamelCase_:      List[Any]=768       ,		UpperCamelCase_:      Any=12       ,		UpperCamelCase_:      List[str]=12       ,		UpperCamelCase_:      Dict=3_072       ,		UpperCamelCase_:      List[str]="gelu"       ,		UpperCamelCase_:      Union[str, Any]=0.0       ,		UpperCamelCase_:      Optional[Any]=0.0       ,		UpperCamelCase_:      List[Any]=0.02       ,		UpperCamelCase_:      str=1E-0_6       ,		UpperCamelCase_:      str=224       ,		UpperCamelCase_:      List[Any]=16       ,		UpperCamelCase_:      int=3       ,		UpperCamelCase_:      int=True       ,		**UpperCamelCase_:      Optional[int]       ,		)     ->  Optional[int]:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_				)

									lowercase__				= hidden_size
									lowercase__				= num_hidden_layers
									lowercase__				= num_attention_heads
									lowercase__				= intermediate_size
									lowercase__				= hidden_act
									lowercase__				= hidden_dropout_prob
									lowercase__				= attention_probs_dropout_prob
									lowercase__				= initializer_range
									lowercase__				= layer_norm_eps
									lowercase__				= image_size
									lowercase__				= patch_size
									lowercase__				= num_channels
									lowercase__				= qkv_bias









'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



